Tutoring Lab - Free tutoring is available for many 1000- and 2000-level courses. Tutors are located in the Computer Lab, MCS room 100. The lab is open Monday through Thursday, 8 a.m. to 8 p.m., and Fridays from 8 a.m. to 4 p.m. The lab is closed on university holidays and operates with limited hours during finals week. No appointment is necessary.
Computer Laboratory - The computer lab is located in room 100, Math and Computer Science building (MCS), and is open to all students. The lab is equipped with up-to-date mathematical software such as Mathematica, Maple, and SAS. The lab is open 8 a.m. - 8 p.m., Mon. - Th. and 8 a.m. - 4 p.m. Friday. The lab is closed on university holidays and operates with limited hours during finals week.
Awards and Scholarships - Each year, the department awards students for their accomplishments. Many awards and scholarships are to honor or in memory of those who have shaped our department into the thriving entity that exists today. Visit the College of Mathematics and Science scholarship page for more information and application instructions.
Open Resource Textbooks - Open-source textbook project for the Department of Mathematics and Statistics.
- Math in Society, by David Lippman at https://www.opentextbookstore.com/mathinsociety/2.5/MathinSociety.pdf (*not all sections)
- Modeling, Functions, and Graphs, by K. Yoshiwara (UCO edition) online at https://ucomath.github.io/mfg
- Modeling, Functions, and Graphs, by K. Yoshiwara (UCO edition) with pdf downloads at https://ucomath.github.io/mfgpdf
- Pre-Calculus at https://openstax.org/details/books/precalculus
- Pre-Calculus, Jay Abramson Sr. (Contributing Author) at https://openstax.org/details/books/precalculus
- Some students may find additional help from the Problems Pack at https://ucomath.github.io/trig
- Calculus Volume 1 (openstax) at https://openstax.org/details/books/calculus-volume-1
- Notes on Diffy Qs: Differential Equations for Engineers at https://www.jirka.org/diffyqs/
- R for Data Science at https://r4ds.had.co.nz/
- An Introduction to Statistical Learning with Applications in R at https://www.statlearning.com/